Methods for battery state estimation can be divided into model-based and data-driven based methods. For model-based approaches, the SoP is estimated from the state of health (SoH) and state of current (SoC) (as well as resistances), and therefore the estimation accuracy depends on the quality of the SoH/SoC estimators. However, achieving accurate SoH/SoC estimation is difficult for batteries with “flat”—Open Circuit Voltage (OCV) characteristics, such as lithium iron phosphate (LiFePO4) battery, Toshiba's rechargeable battery (SCiB), nickel-metal hydride (NiMH) battery, because of poor observability of the SoH and SoC from the measurements, among other things. The direct estimation of the SoP from the measurements can have stronger observability, nevertheless there are no known models of today, which are physically understandable. We are not aware of data-driven methods for battery SoP estimation for today's applications. This is probably due to the fact that battery SoP estimation has received a lot less attention than battery SoC and SoH estimation, and thus, why we are not aware of any known data-driven methods for battery SoP estimation.
Therefore, there is great importance and technological need for data-driven methods for battery SoP estimation for battery management systems.